非完备信息下的超视距空战双机协同战术识别

Cooperative tactical recognition of dual-aircraft formation under incomplete information in BVR air combat

针对超视距(BVR)空战过程中,受探测装置性能限制和敌方干扰等原因,导致目标信息易缺失,从而难以实时准确地识别敌方协同空战战术的问题,提出了一种基于动态贝叶斯网络(DBN)与参数学习的超视距空战双机协同战术识别方法。分析了超视距空战条件下的双机协同战术特征,根据长机和僚机的职能分工、当前态势及机动动作,构建了识别网络模型;为提高模型对双机协同战术的识别概率,采用期望最大参数学习方法优化网络参数;基于自回归模型对缺失目标信息进行修补,提出非完备信息下的双机协同战术识别推理算法。通过开展空战对抗仿真实验,验证了双机协同战术识别方法对于非完备信息下的超视距空战双机协同战术具有较高的识别概率和较好的实时性。

In the process of beyond-visual-range(BVR)air combat,due to the limitation of detection equipment performance and enemy interference,the target information is easy to get lost,which makes it difficult to identify the enemy’s cooperative air combat tactics in real time.A method of cooperative tactical recognition is proposed based on dynamic Bayesian network(DBN)and parameter learning.Firstly,the cooperative tactics of dual-aircraft formation in BVR air combat are analyzed.According to the functional tasks of leader and wingman,the current situation information and fighter maneuver,a DBN recognition model is established.Then,to improve the recognition rate of the model,the expected maximum parameter learning method is used to optimize the network parameters.Finally,based on the auto-regressive model,the missing target information is repaired,and the reasoning algorithm of cooperative tactical recognition under incomplete information is proposed.The simulation results show that the method of cooperative tactical recognition has high recognition accuracy and good real-time performance for cooperative tactics under incomplete information in BVR air combat.